NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Molecular characterization of the human gut microbiota: the effect of aging

Age-related physiological changes in the gastrointestinal tract, as well as modification in lifestyle, nutritional behaviour, and functionality of the host immune system, inevitably affect the gut microbiota. The study presented here is focused on the application and comparison of two different microarray approaches for the characterization of the human gut microbiota, the HITChip and the HTF-Microb.Array, with particular attention to the effects of the aging process on the composition of this ecosystem. By using the Human Intestinal Tract Chip (HITChip), recently developed at the Wageningen University, The Netherland, we explored the age-related changes of gut microbiota during the whole adult lifespan, from young adults, through elderly to centenarians. We observed that the microbial composition and diversity of the gut ecosystem of young adults and seventy-years old people is highly similar but differs significantly from that of the centenarians. After 100 years of symbiotic association with the human host, the microbiota is characterized by a rearrangement in the Firmicutes population and an enrichment of facultative anaerobes. The presence of such a compromised microbiota in the centenarians is associated with an increased inflammation status, also known as inflamm-aging, as determined by a range of peripheral blood inflammatory markers. In parallel, we overtook the development of our own phylogenetic microarray with a lower number of targets, aiming the description of the human gut microbiota structure at high taxonomic level. The resulting chip was called High Taxonomic level Fingerprinting Microbiota Array (HTF-Microb.Array), and was based on the Ligase Detection Reaction (LDR) technology, which allowed us to develop a fast and sensitive tool for the fingerprint of the human gut microbiota in terms of presence/absence of the principal groups. The validation on artificial DNA mixes, as well as the pilot study involving eight healthy young adults, demonstrated that the HTF-Microb.Array can be used to successfully characterize the human gut microbiota, allowing us to obtain results which are in approximate accordance with the most recent characterizations. Conversely, the evaluation of the relative abundance of the target groups on the bases of the relative fluorescence intensity probes response still has some hindrances, as demonstrated by comparing the HTF.Microb.Array and HITChip high taxonomic level fingerprints of the same centenarians.

Researches on berry composition in red grape: agronomical, biochemical and molecular approaches

It is well known that the best grape quality can occur only through the achievement of optimal source/sink ratio. Vine balance is in fact a key parameter in controlling berry sugar, acidity and secondary metabolites content (Howell, 2001; Vanden Heuvel et al., 2004). Despite yield reduction and quality improvement are not always strictly related, cluster thinning is considered a technique which could lead to improvement in grape sugar and anthocyanin composition (Dokoozlian and Hirschfelt, 1995; Guidoni et al., 2002). Among several microclimatic variables which may impact grape composition, the effect of cluster light exposure and temperature, which probably act in synergistic and complex way, has been widely explored showing positive even sometimes contradictory results (Spayd et al., 2001; Tarara et al., 2008). Pre-bloom and véraison defoliation are very efficient techniques in inducing cluster microclimatic modification. Furthermore pre-bloom defoliation inducing a lower berry set percentage On these basis the aim of the first experiment of the thesis was to verify in cv Sangiovese the effects on ripening and berry composition of management techniques which may increase source/sink ratio and /or promote light incidence on berries throughout grape ripening. An integrated agronomic, biochemical and microarray approach, aims to understand which mechanisms are involved in berry composition and may be conditioned in the berries during ripening in vines submitted to three treatments. In particular the treatments compared were: a) cluster thinning (increasing in source/sink ratio) b) leaf removal at véraison (increasing cluster light exposure) c) pre-bloom defoliation (increasing source sink ratio and cluster light exposure). Vine response to leaf removal at véraison was further evaluated in the second experiment on three different varieties (Cabernet Sauvignon, Nero d’Avola, Raboso Piave) chosen for their different genetic traits in terms of anthocyanin amount and composition. The integrated agronomic, biochemical and microarray approach, employed in order to understand those mechanisms involved in berry composition of Sangiovese vines submitted to management techniques which may increase source/sink ratio and induce microclimatic changes, bring to interesting results. This research confirmed the main role of source/sink ratio in conditioning sugars metabolism and revealed also that carbohydrates availability is a crucial issue in triggering anthocyanin biosynthesis. More complex is the situation of pre-bloom defoliation, where source/sink and cluster light increase effects are associated to determine final berry composition. It results that the application of pre-bloom defoliation may be risky, as too much dependent on seasonal conditions (rain and temperature) and physiological vine response (leaf area recovery, photosynthetic compensation, laterals regrowth). Early induced stress conditions could bring cluster at véraison in disadvantage to trigger optimal berry ripening processes compared to untreated vines. This conditions could be maintained until harvest, if no previously described physiological recovery occurs. Certainly, light exposure increase linked to defoliation treatments, showed a positive and solid effect on flavonol biosynthesis, as in our conditions temperature was not so different among treatments. Except the last aspects, that could be confirmed also for véraison defoliation, microclimatic changes by themselves seemed not able to induce any modification in berry composition. Further studies are necessary to understand if the peculiar anthocyanic and flavonols composition detected in véraison defoliation could play important role in both color intensity and stability of wines.